1. Field of the Invention
The present invention relates a method and apparatus for automatically locating a region of interest in a radiograph, and more particularly to a method and apparatus for automatically locating a region of interest in a digitized mammogram.
2. Description of the Prior Art
A potential advantage of digital radiography is the capability for quantitative analysis of image data representing normal and abnormal patterns, and the subsequent use of this data to aid radiologists' diagnoses. For example, digital image analysis techniques are known for detecting microcalcifications in mammograms, lung nodules in chest radiographs and for tracking opacified vessels, as well as use in other types of imaging modalities. Since digital image analysis techniques tend to be computationally intensive, and the number of image elements (pixels) in a digital image typically ranges from a minimum of several hundred thousand to tens of millions, digital image analysis tends to be a time consuming and cumbersome "number crunching" process.
One way to reduce the computational demands required for digital image analysis is to reduce the area of the digital image that is analyzed to only a desired region of interest (ROI). By processing only the image intensity data in the digital image that is within the ROI, digital image analysis can be accomplished more efficiently and effectively, and thereby made economically possible for uses such as large scale radiographic image screening of the public for biological abnormalities. For example, since radiographic images typically contain intensity data corresponding to portions other than the patient being imaged, such as the name of the patient, the name of the doctor, and the name of the facility where the radiographic images were generated, these portions of the radiographic image should not be processed by the digital image analysis techniques used for detecting the biological abnormalities, thereby saving a substantial amount of image processing resources and time. Thus, the desired ROI, for example the image of the breast in a mammogram, should be selected as fast as possible, and preferably automatically, with minimum or no user intervention and with a minimum amount of signal processing.
Past attempts to determine regions of interest in digital radiographs include both manual and automatic techniques as well as combined manual/automatic techniques. As is well known, manual techniques require, for example, the user to view the digital image on a computer display, and to mark the ROI, the breast boundary, using, i.e., a light pen. Completely manual techniques for finding an ROI are undesirable due to the extent of manual intervention by the user. U.S. Pat. No. 4,851,984 relates to an automatic method for finding an ROI, and in particular, inter-rib spaces for lung texture analysis. However, in view of the lack of "ribs" in mammographic images, the processing described in the '984 patent is more complex than is required for finding an ROI in a less complex image, such as in a mammogram. Patents have also issued which relate to image processing techniques for digital mammograms wherein the image intensity data is analyzed for the purpose of detecting microcalcifications, such as U.S. Pat. Nos. 5,491,627 and 4,907,156. However, all of the digital image data is processed by the technique described in these patents so as to perform a very high spatial resolution analysis of the digital mammogram, a very computationally complex process. An automatic and reliable ROI finder for subsequent use by such image processing techniques is not shown or suggested thereby.
U.S. Pat. No. 5,452,367 describes an automatic method for finding a desired region of interest in a medical image by analyzing a global histogram of intensity values representative of the medical image. Unfortunately, this technique is very computationally intensive. It needs every one of the potentially tens of millions of intensity values in the medical image, to 1) smooth each pixel with a median filter, 2) calculate the range of neighboring intensity values around each pixel, 3) calculate an intensity histogram over all pixels, 4) calculate a second histogram over all pixels with a small range of neighboring intensity values, and 5) make a full-size 3-level mask image indicating a) dark pixels with small range of neighboring intensity values, b) pixels with large range of neighboring intensity values, and c) bright pixels with small range of neighboring intensity values.
It would be desirable to provide an efficient and effective ROI finder, especially for mammographic ROI's, that permits high spatial resolution digital image analysis to be performed only on the ROI, and that can be implemented into a large-scale screening program for the general public. Accordingly, an automatic technique for more simply locating a desired ROI in a mammographic image would be particularly useful. As will be explained below, the present invention applies techniques which permit a significant down-sampling of the image without filtering, resulting in the simple, direct use of e.g. only 11,000 pixels out of a medical image of 45,000,000 pixels, and run-times on a standard workstation of the order of 1 to 2 seconds per image.
Reliable identification of a desired ROI in a digitized image, such as a mammogram, is not straightforward, in view of variations in the following: film response, the bit-depth of the image digitizing scanner, the resolution of the image digitizing scanner, the total size of the image, clustering of image intensity levels, image statistics which are sub-sampled for speed, prior probability of the desired signal (i.e., the body signal) versus background noise, divergence of noise and signal intensity levels, the presence, orientation and size of identifying name tags, etc., overlap of objects in the image, number of objects in the image, and uniformity of density in the region of interest.
It would be desirable to provide a less computationally complex ROI finder which will accurately identify a desired ROI in a radiographic image, work with digitized radiographic images of any size, including those having a wide variation in signal to noise level, and specifically eliminate non-desired objects, such as name tags, etc. located therein.